PASS GUARANTEED RELIABLE GOOGLE - PROFESSIONAL-CLOUD-DEVOPS-ENGINEER - GOOGLE CLOUD CERTIFIED - PROFESSIONAL CLOUD DEVOPS ENGINEER EXAM EXAM QUESTIONS PDF

Pass Guaranteed Reliable Google - Professional-Cloud-DevOps-Engineer - Google Cloud Certified - Professional Cloud DevOps Engineer Exam Exam Questions Pdf

Pass Guaranteed Reliable Google - Professional-Cloud-DevOps-Engineer - Google Cloud Certified - Professional Cloud DevOps Engineer Exam Exam Questions Pdf

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In this Desktop-based Google Professional-Cloud-DevOps-Engineer practice exam software, you will enjoy the opportunity to self-exam your preparation. The chance to customize the Google Professional-Cloud-DevOps-Engineer practice exams according to the time and types of Google Professional-Cloud-DevOps-Engineer practice test questions will contribute to your ease. This format operates only on Windows-based devices. But what is helpful is that it functions without an active internet connection. It copies the exact pattern and style of the real Google Professional-Cloud-DevOps-Engineer Exam to make your preparation productive and relevant.

Test Structure

The candidates who want to take this Google exam will have two hours to answer all questions. Even though the vendor doesn’t give details on the total number of questions that the examinees will receive, they should be prepared to solve multiple-choice and multiple-answer inquiries. Besides, the test is delivered in the English language only. As for the registration fee, the test-takers will need to pay $200 to take it. Additional taxes may apply depending on the candidate’s profile and chosen delivery method. By and large, the applicants have two options to take the official exam. They can choose to take it online from any remote location that they prefer. If they choose this option, the candidates should read carefully what the testing requirements are. In case applicants prefer to be present in a classroom when they take the actual testing, then they can search for a test center that is closest to their location. Also, Google doesn’t have any prerequisites for the candidates to be eligible for the evaluation. Still, it recommends that the candidates for the Professional Cloud DevOps Engineer exam should have at least 3 years of experience in the industry including a minimum of one year of experience in managing and developing solutions on GCP.

How to Prepare For Google Professional Cloud DevOps Engineer Exam

Preparation Guide for Google Professional Cloud DevOps Engineer Exam

Introduction

Google has designed a track for IT professionals to endorse as a cloud DevOps Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using the industry-standard methodology to conclude whether or not an aspirant meets Google's proficiency standards.

According to Google, a Google Professional Cloud DevOps Engineer Exam test facilitates organizations to influence Google Cloud technologies. By leveraging experience implementing VPCs, DevOps services, hybrid connectivity, and security for established DevOps architectures, this individual ensures successful cloud implementations using the Google Cloud Platform Console or the command-line interface.

Certification is evidence of your skills, expertise in those areas in which you like to work. If a candidate wants to work as Google Professional Cloud DevOps Engineer and prove his knowledge, certification is offered by Google. This Google Professional Cloud DevOps Engineer Certification helps a candidate to validates his skills in Google Professional Cloud DevOps Engineer Technology.

In this guide, we will cover the Google Professional Cloud DevOps Engineer practice exams, Google Professional Cloud DevOps Engineer Certified Professionals salary, and all aspects of the Google Professional Cloud DevOps Engineer Certification.

To be eligible for the Professional-Cloud-DevOps-Engineer Certification Exam, candidates need to have at least three years of experience in cloud computing, software development, and DevOps practices. They also need to have a good understanding of GCP services and tools, including Compute Engine, Kubernetes Engine, Cloud Storage, Cloud SQL, Cloud Build, and Cloud Monitoring. Candidates are recommended to take the GCP Associate Cloud Engineer and GCP Professional Cloud Architect certification exams before attempting the Professional-Cloud-DevOps-Engineer certification exam.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

  • A. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.
  • B. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
  • C. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness probes.
  • D. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.

Answer: D

Explanation:
https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics The Google Cloud HTTP Load Balancer (GCLB) provides metrics on the number of requests and the response latency for each backend service. These metrics can be used as custom metrics for the horizontal pod autoscaler (HPA) to scale the deployment based on the load. This is the correct solution to use an appropriate SLI for scaling.


NEW QUESTION # 113
You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?

  • A. Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
  • B. In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.
  • C. Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.
  • D. Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.

Answer: C


NEW QUESTION # 114
You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?

  • A. Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.
  • B. Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.
  • C. Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table
  • D. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.

Answer: D

Explanation:
Explanation
https://cloud.google.com/logging/docs/logs-based-metrics#counter-metric


NEW QUESTION # 115
Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE) The application load increases during the day and decreases during the night Your operations team has configured the application to run enough Pods to handle the evening peak load You want to automate scaling by only running enough Pods and nodes for the load What should you do?

  • A. Configure the Horizontal Pod Autoscaler but keep the node pool size static
  • B. Configure the Horizontal Pod Autoscaler and enable the cluster autoscaler
  • C. Configure the Vertical Pod Autoscaler but keep the node pool size static
  • D. Configure the Vertical Pod Autoscaler and enable the cluster autoscaler

Answer: B

Explanation:
The best option for automating scaling by only running enough Pods and nodes for the load is to configure the Horizontal Pod Autoscaler and enable the cluster autoscaler. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. The cluster autoscaler is a feature that automatically adjusts the size of a node pool based on the demand for node capacity. By using both features together, you can ensure that your application runs enough Pods to handle the load, and that your cluster runs enough nodes to host the Pods. This way, you can optimize your resource utilization and cost efficiency.


NEW QUESTION # 116
Your company is developing applications that are deployed on Google Kubernetes Engine (GKE) Each team manages a different application You need to create the development and production environments for each team while you minimize costs Different teams should not be able to access other teams environments You want to follow Google-recommended practices What should you do?

  • A. Create a development and a production GKE cluster in separate projects In each cluster create a Kubernetes namespace per team and then configure Identity-Aware Proxy so that each team can only access its own namespace
  • B. Create a development and a production GKE cluster in separate projects In each cluster create a Kubernetes namespace per team and then configure Kubernetes role-based access control (RBAC) so that each team can only access its own namespace
  • C. Create one Google Cloud project per team In each project create a cluster with a Kubernetes namespace for development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters.
  • D. Create one Google Cloud project per team In each project create a cluster for development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters

Answer: B

Explanation:
Explanation
The best option for creating the development and production environments for each team while minimizing costs and ensuring isolation is to create a development and a production GKE cluster in separate projects, in each cluster create a Kubernetes namespace per team, and then configure Kubernetes role-based access control (RBAC) so that each team can only access its own namespace. This option allows you to use fewer clusters and projects than creating one project or cluster per team, which reduces costs and complexity. It also allows you to isolate each team's environment by using namespaces and RBAC, which prevents teams from accessing other teams' environments.


NEW QUESTION # 117
......

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